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The purpose of this paper is to analyze the supply chain's resilience in crowd networks from both static and dynamic perspectives.

This paper first defines the supply chain’s resilience, then proposes a graphical and game-theoretic framework to evaluate the resilience.

In this framework, an equilibrium with high resilience will be achieved after the iterated prisoner's dilemma in the supply chain. The two-stage update mechanism contributes to higher profits, higher stability and stronger risk resistance capability. The reputation-based tit-for-tat strategy in the second stage helps to realize society cooperation.

This work pays more attention to the dynamic evolution of interactions between organizations in the supply chain. It provides an important theoretical basis for future work such as how to effectively control and guide the evolution of events in the intelligence network and how to stand sudden changes and avoid collapse.

With the popularity of the internet and the increasing numbers of netizens, tremendous information flows are generated daily by the intelligently interconnected individuals. The diffusion processes of different information are not independent, and they interact with and influence each other. Modeling and analyzing the interaction between correlated information play an important role in the understanding of the characteristics of information dissemination and better control of the information flows. This paper aims to model the correlated information diffusion process over the crowd intelligence networks.

This study extends the classic epidemic susceptible–infectious–recovered (SIR) model and proposes the SIR mixture model to describe the diffusion process of two correlated pieces of information. The whole crowd is divided into different groups with respect to their forwarding state of the correlated information, and the transition rate between different groups shows the property of each piece of information and the influences between them.

The stable state of the SIR mixture model is analyzed through the linearization of the model, and the stable condition can be obtained. Real data are used to validate the SIR mixture model, and the detailed diffusion process of correlated information can be inferred by the analysis of the parameters learned through fitting the real data into the SIR mixture model.

The proposed SIR mixture model can be used to model the diffusion of correlated information and analyze the propagation process.

The purpose of this paper is to review the recent studies on opinion polarization and disagreement.

In this work, recent advances in opinion polarization and disagreement and pay attention to how they are evaluated and controlled are reviewed.

In literature, three metrics: polarization, disagreement and polarization-disagreement index are usually adopted and there is a tradeoff between polarization and disagreement. Different strategies have been proposed in literature which can significantly control opinion polarization and disagreement based on these metrics.

This review is of crucial importance to summarize works on opinion polarization and disagreement and to the better understanding and control of them.

Autonomous driving depends on the collection, processing and analysis of environmental information and vehicle information. Environmental perception and processing are important prerequisite for the safety of self-driving of vehicles; it involves road boundary detection, vehicle detection, pedestrian detection using sensors such as laser rangefinder, video camera, vehicle borne radar, etc.

Subjected to various environmental factors, the data clock information is often out of sync because of different data acquisition frequency, which leads to the difficulty in data fusion. In this study, according to practical requirements, a multi-sensor environmental perception collaborative method was first proposed; then, based on the principle of target priority, large-scale priority, moving target priority and difference priority, a multi-sensor data fusion optimization algorithm based on convolutional neural network was proposed.

The average unload scheduling delay of the algorithm for test data before and after optimization under different network transmission rates. It can be seen that with the improvement of network transmission rate and processing capacity, the unload scheduling delay decreased after optimization and the performance of the test results is the closest to the optimal solution indicating the excellent performance of the optimization algorithm and its adaptivity to different environments.

In this paper, the results showed that the proposed method significantly improved the redundancy and fault tolerance of the system thus ensuring fast and correct decision-making during driving.

With the continuous technological development of automated driving and expansion of its application scope, the types of on-board equipment continue to be enriched and the computing capabilities of on-board equipment continue to increase and corresponding applications become more diverse. As the applications need to run on on-board equipment, the requirements for the computing capabilities of on-board equipment become higher. Mobile edge computing is one of the effective methods to solve practical application problems in automated driving.

In this study, in accordance with practical requirements, this paper proposed an optimal resource management allocation method of autonomous-vehicle-infrastructure cooperation in a mobile edge computing environment and conducted an experiment in practical application.

The design of the road-side unit module and its corresponding real-time operating system task coordination in edge computing are proposed in the study, as well as the method for edge computing load integration and heterogeneous computing. Then, the real-time scheduling of highly concurrent computation tasks, adaptive computation task migration method and edge server collaborative resource allocation method is proposed. Test results indicate that the method proposed in this study can greatly reduce the task computing delay, and the power consumption generally increases with the increase of task size and task complexity.

The results showed that the proposed method can achieve lower power consumption and lower computational overhead while ensuring the quality of service for users, indicating a great application prospect of the method.

The purpose of this paper is to take second-hand vehicles at judicial auctions in China as the primary research direction and to explore the impact of purchasing restriction policy and city size on the relationship between the appraisal price and transaction price of second-hand motor vehicles in the context of auto purchase restriction in China from a microscopic angle. It attempts to broaden the pricing ideas of judicial appraisal enterprises in providing appraisal prices of second-hand motor vehicles and to put forward suggestions for the optimization of appraisal prices and appraisal standards of judicial appraisal enterprises.

With the help of Python, this paper crawls 59,038 lines of valid data from three leading internet judicial auction platforms, namely “Ali Auction,” “China Beijing Equity Exchange” and “Gong Pai Wang,” as research samples. Besides, this paper forms a database containing judicial auction used car appraisal prices, transaction prices, motor vehicle purchase restrictions and whether the motor vehicle carries a license plate. By constructing a multiple regression model, the impact of automobile purchase restriction policy on the price of motor vehicles appraised by judicial appraisal enterprises is investigated.

With the help of the multivariate regression model, it found that under the same condition, the city where the auction took place implemented the automobile purchase restriction before the end of the auction. The court has specified that the buyer could directly obtain the license plate after the auction. The transaction price and the evaluation price ratio will be statistically larger, which proves that the license plate has an evident value in the transaction and is traded as subject matter by the residents, and consequently brings a higher premium to the price of automobile transaction in internet judicial auction. Meanwhile, the purchase restriction policy in the first-tier cities has resulted in a significant premium on automobile license plates, which is much higher than the automobile license plate premium level in non-first-tier cities under the same conditions.

Car ownership continues to rise with rapid economic development worldwide. Control the growth of car ownership, some countries and regions mainly restrict the issuance of motor vehicle license plates, which indirectly leads to vehicle license plate indicators becoming a scarce resource. National laws permit judicial auction as a means for the people's courts to settle creditors' claims in enforcement procedures of civil cases. In the judicial auction process, the People's Court introduces third-party evaluation enterprises to appraise, assess and audit the subject and obtain the appraisal price, which guides the bidding behavior of used car buyers and indirectly affects the transaction price of used cars.

As the only subject capable of assessing the value of used cars at judicial auctions, judicial appraisal enterprises have received widespread attention for their appraisal results. This paper researches this field by screening the factors affecting the ratio of motor vehicle transaction price to the appraised price. It also analyzes how the ratio of motor vehicle transaction price to appraised price is affected by motor vehicle purchase restrictions and the situation with license plates. This paper examines the existence of premiums for motor vehicle transactions with license plates, evaluates the purchase restrictions in cities with motor vehicle purchase restrictions and verifies that the premiums for motor vehicles at judicial auctions are affected by purchase restriction policies as well as the influence of city class. These studies have important implications for judicial appraisal enterprises to establish reasonable appraisal mechanisms and optimize appraisal prices. They also provide new ideas and methods for appraisal enterprises to assess the value of used vehicles at judicial auctions.

China has become an economic powerhouse in historic terms but there are a number of challenges to its continued prosperity. The aim of this paper is to more fully understand China's propensity for creative innovation, which is seen as an important next stage in its continued development.

The paper is conceptual but uses historical and secondary data to support its assumptions. The paper was written in conjunction with the 1st Global Peter F. Drucker Forum (celebrating 100 years since his birth) and attempts to continue his challenge of “the hard work of thinking”.

China has a long history of successful innovation. However, Confucian belief, a single despot and a closing off to the rest of the world have thwarted its innovative edge. The key to rekindling the entrepreneurial spirit is seen largely as an internal battle based on the state's ability to balance the institution of government with the needs of a burgeoning prospective creative class. This paper identifies that much of this change will rely on quality‐related developments rather than simply investments of financial capital.

The ability to create new things is a challenge to developing economies that rely on low cost and imitation. China's success in innovation will have substantial implications for developed nations both economically and geo‐politically. China wants to be a significant player on a global scale and this paper sheds light on its potential to achieve such an objective. Through traversing China's innovative landscape, this paper also enlightens the field of management on key aspects of China's innovative past, present and future.

The silicone modifications of two-component epoxy resin coatings are commonly built on epoxy resins rather than on epoxy curing agents. The silicone-modified epoxy curing agent system is rarely reported yet. This study aims to prepare the polysiloxane (PS)-modified waterborne epoxy coatings based on aqueous curing agents technology.

Waterborne epoxy curing agents with different contents of terminal epoxy PS were synthesized by reacting with triethylenetetramine, followed by incorporating of epoxy resin (NPEL-128) and polyethylene glycol diglycidyl ether. The waterborne epoxy coatings were prepared with the above curing agents, and their performance was investigated through thermogravimetric analysis, scanning electron microscopy, mechanical characterization, gloss measurement, chemical resistance test and ultraviolet (UV) aging experiment.

The results showed that the epoxy coating prepared by silicon-modified curing agent has higher gloss, better chemical resistance and UV resistance than the coating from unmodified curing agent with terminal epoxy PS and commercially available waterborne epoxy curing agent (Aradur 3986), as well as the competitive mechanical properties and heat resistance. Reduced water absorption on fibrous paper was also obtained with the help of silicon-modified curing agent.

These findings will be valuable for resin researchers in addressing the modification issues about waterborne epoxy resin and curing agent.

The purpose of this paper is to solve new pricing issues faced by low-carbon companies in the Yellow River Basin, which is caused by the change of key pricing factors in the mixed appliance background of Big Data and blockchain, such as product quality and carbon-emission reduction CER level (hereafter, CER level).

We choose a low-carbon supply chain with a low-carbon manufacturer and a retailer as our research object. Then, we propose that using the ineffective effect of the CER level and the quality and safety level to reflect the relationships among the CER level, the quality and safety level and the market demand is more suitable in the new environment. Based on these, we revise the demand equation. Afterwards, by using Stackelberg game, four cost-sharing situations and their pricing rules are analyzed.

Results indicated that in the four cost-sharing situations, the change trends and the magnitudes of the best retail prices were not affected by the changes of the inputs of the demand information and the traceability services costs (hereafter, DITS costs), the proportion about retailer's DITS costs undertaken by the manufacturer, the ineffective effect coefficient of the CER level and the quality and safety level and the cost optimization coefficient. However, the cost-sharing situations could affect the change magnitudes of the best revenues.

This paper has two main contributions. First, this paper proposes a demand function that is more suitable for the mixed appliance background of Big Data and blockchain. Secondly, this paper improves the cost-sharing model and finds that demand information sharing and traceability service sharing have different impacts on key pricing factors of low-carbon product. In addition, this research provides a theoretical reference for low-carbon supply chain members to formulate pricing strategies in the new background.